Predictability of Time-varying Jump Premiums: Evidence Based on Calibration
Kent Wang, Yuqiang Guo
The Australian Journal of Management
#002192 20131014 (published)
This study supplies new evidence regarding the predictive power of jumps for conditional market returns and volatilities. We change the constant jump intensity as in the LPW and Du models with time-varying intensity following an autoregressive conditional jump intensity (ARJI) process and a squared bessel (SB) process, and apply calibrated jump premiums to predict excess market returns and volatilities. We show that all calibrated jump premiums have significant predictive power in sample and out-of-sample. We find that in the U.S. market LPW’s model forecasts excess returns and volatilities better. The ARJI process of jump intensity predicts excess returns better, and SB process forecasts volatilities better. In the Australian market we find that, the model with ARJI process of jump intensity predicts Australian market returns and volatilities better.
JEL-Codes: C13; C14; G10; G12
Keywords: Jump intensity; Equity premium; Jump premium; Stock return predictability; Volatility predictability

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